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Towards a Data Driven, Scalable and Intelligent Industrial Demand Response: AI, Automation, and the Computing Continuum
No full textIndustrial Demand Response (IDR) systems have emerged as a key enabler for enhancing grid flexibility, particularly as industries face increasing pressure to optimize energy consumption and integrate with renewable energy sources. However, despite their potential, the adoption and scalability of IDR solutions are limited by a range of technical, infrastructural, and organizational challenges. This dissertation investigates how emerging digital technologies—namely, Artificial Intelligence/Machine Learning (AI/ML) and the computing continuum (edge, fog, and cloud computing)—can be leveraged to overcome these limitations and enable scalable, intelligent, and interoperable IDR architectures.
The study addresses three core research questions. First, it develops a taxonomy of barriers to IDR adoption, distinguishing between technological and non-technological constraints. Second, it explores how distributed computing paradigms can mitigate these challenges by enabling real-time, privacy-aware, and latency-sensitive decision-making across industrial sites. Third, it examines the synergistic integration of AI/ML within the computing continuum, emphasizing methods such as federated learning, transfer learning, and multi-agent reinforcement learning to overcome issues related to data sparsity, system complexity, and semantic heterogeneity.
A reference architecture for IDR aggregators is proposed, combining layered intelligence, semantic interoperability, and orchestration mechanisms. This architecture is mapped to real-world cloud and open-source platforms to demonstrate its practical applicability. The findings confirm that the integration of AI/ML and distributed computing is not only feasible but essential for advancing the resilience, autonomy, and responsiveness of future industrial energy systems
Microbial insights into ocean alkalinity enhancement: Bacterial community risk assessment and the benefit of increasing research on carbonic anhydrase
No full textClimate change driven by anthropogenic CO₂ emissions requires effective mitigation strategies. Negative emission technologies (NETs), particularly ocean alkalinity enhancement (OAE), are promising because they increase ocean alkalinity and promote CO₂ sequestration. This dissertation examines how marine molecular biology can help assess ecological risks and the overall efficacy of OAE. It presents two risk assessments on bacterial community responses to alkalinity exposure and develops a framework for a novel biological proxy for monitoring, reporting, and verification (MRV) in OAE.
Chapter 1 provides a general introduction.
Chapter 2 investigates how gradually increased alkalinity affects pelagic bacterial communities using a mesocosm experiment with 16S rRNA gene sequencing and flow cytometry. Results show high structural resilience, but quantitative shifts in bacterial abundance linked to phytoplankton dynamics indicate indirect ecological effects of OAE.
Chapter 3 expands this work by comparing two OAE strategies: olivine dissolution and direct dissolved alkalinity addition. A mesocosm experiment assessed microbial responses in seawater and oyster gills (Ostrea edulis). Olivine increased pollution-tolerant and biofilm-forming taxa, while dissolved alkalinity caused minimal change. These findings suggest that dissolved alkalinity below 500 µmol L⁻¹ is a relatively safe OAE approach.
Chapter 4 proposes carbonic anhydrase (CA), a key enzyme in marine carbon cycling, as a biological proxy for evaluating OAE performance. Structured hypotheses outline how CA expression and activity assays could support future OAE MRV systems. The chapter recommends shifting resources from broad bacterial community assessments toward investigating how alkalization affects CA
Towards Sustainable Fisheries Management: Understanding Territorial Use Rights in Fisheries (TURF) and Environmental Stewardship Actions
No full textLocal fishers have historically been subjected to the detrimental effects of overfishing, a consequence of open access practices that are likely to persist in the coming decades. The open-access nature of many fisheries is a significant driver of overfishing, which poses a substantial threat to marine ecosystems and their livelihoods that depend on them. The concept of common property theory provides a theoretical framework that can be used to explain the phenomenon of overfishing due to open access practices. Fishery resources are regarded as examples of a common property, implying that these resources belong to all fishers. This assumption gives rise to intense competition among fishers to exploit the fishery resources. One potential solution to this problem is to establish a territorial use rights system (TURF) that would prevent open-access practices.
This thesis argues that all relevant stakeholders in small-scale fisheries management should prioritize environmental stewardship, regardless of the system used to address the overfishing problem caused by open-access practices, as this constitutes a principal factor in determining sustainability. This perspective is particularly relevant in the context of TURF implementation as numerous studies have demonstrated that TURF is an effective means of fostering stewardship. This thesis presents a collection of three studies that address the two primary topics of TURF and stewardship. While this thesis is primarily based on a case study of TURF implementation and stewardship actions (fishing logbook) in Kepulauan Seribu, Indonesia, I hope that the resulting publications will serve as additional references and contribute to the global discussion on TURF and stewardship
Advanced Beamforming Techniques for Enhanced Flexibility, Accessibility, and Multi-functionality in Wireless Communications Systems
No full textThis dissertation contributes to three distinct focuses of BF design as: BF for flexible connectivity, BF for enhanced connectivity, and BF for over-the-air-computating (AirComp). The first objective is the “BF for flexible connectivity”. In order to realize flexibility for preserving connectivity regardless of the user position in the coverage area, the recent cell-free MIMO (CF-MIMO) system is considered. For the BF design, a flexible design is proposed, which is directly adaptable not only for both uplink (UL) and downlink (DL) communication modes but also for both under-loaded and over-loaded scenarios. As for the second focus, termed “BF for enhanced connectivity,” a novel BF design compatible with three distinct power allocation schemes is proposed. For the sake of connectivity enhancement, a DL MIMO-rate splitting multiple access (RSMA) system is considered. Even though the proposed BF design is assumed to be used only for under-loaded or fully-loaded scenarios, the computational complexity required to design BF is significantly less than that of the state-of-the-art (SotA) alternative. Finally, the ”BF for Over the Air Computing” is considered for envisioning the realization of integrated AI and communication. In this focus, novel receiver (RX) BF designs compatible with uniform-forcing (UF) precoding for a multi-user UL multiple-input single-output (MISO)-AirComp system are considered for higher performance or lower complexity. Toward higher performance design, while the proposed design sacrifices computational complexity in the BF, the resulting AirComp has a lower mean square error(MSE) performance. On the other hand, the proposed BF design for lower complexity realizes equivalent MSE performance achieved by the high-performance BF design with significantly lower complexity thanks to the combination of recent convex optimization and Bayesian optimization (BO) methods
Minimal models of dynamics on graphs to study generic structural-functional connectivity relationships
No full textThe relationship between network structure (structural connectivity, SC) and network representations of dynamics (functional connectivity, FC) is a topic of high scientific interest both for advancing theoretical understanding of complex systems and for its relevance to a wide range of applications.
In this thesis, correlations between structural and functional connectivity were investigated distinguishing between synchronous and sequential activity of the nodes. The primary analysis encompasses applying different dynamical models to network architectures to explore how SC/FC correlations are shaped by variations in network topology, coupling strength, and intrinsic system parameters across excitable, chaotic, and oscillatory dynamics. A more detailed investigation was conducted on regular graphs of coupled logistic maps. Symbolic encoding of the initial dynamics was used to construct equivalent cellular automaton models, followed by an analysis of the structure of their resulting attractors. The influence of noise on SC/FC correlations was also explored. Finally, SC and the two types of FC were conceptualized in a hydrological case study. Structural and functional networks were constructed from data collected in the Walnut Gulch Experimental Watershed (Arizona, USA). SC/FC correlations served as metrics to describe event-level hydrological responses of the watershed after various rainfall events, and their relationships to hydrological quantities were analyzed
The Relevance of Due Diligence, Hard and Soft Information in Financing Small Firms in Ghana
No full textSmall business lending is significantly influenced by the interplay between the information institution types, the information models, and the due diligence process undertaken by financial institutions. While previous research has examined the impact of hard and soft information on credit availability, limited attention has been given to how different combinations of these information types and various financial institution types influence the quality of loan applications and the loan application success rates. Additionally, the role of due diligence in assessing small business loan applications and the specific signals lenders rely on for decision-making remain underexplored.
This study integrates insights from three research streams to provide a unique analysis of small business lending dynamics. First, based on primary data collected from 242 small firms in Ghana and considering different financial institutions, including non-banks, we examine the effect of three distinct combinations of hard and soft information on loan application success rates. Our findings challenge conventional wisdom, indicating that an increased emphasis on soft information does not necessarily enhance transparency or improve loan application success rates. Moreover, while small-sized banks positively influence credit availability, this effect does not extend to small-sized non-banks.
Second, leveraging signal theory, we analyze the due diligence process undertaken by financial institutions through qualitative insights from 24 loan officer interviews. We identify 11 key hard and soft signals that influence credit risk assessments, including key person risk, change in leadership risk, articulation of company value, adaptation to change, data accuracy, and completeness. These insights highlight the multifaceted nature of credit decision-making
Rare Earth Elements in the Environment and Their Transfer Across the Hydrosphere-Biosphere Interface: Examples from Freshwater and Marine Systems
No full textOur modern society relies heavily on the availability and utilisation of rare earth elements and yttrium (REY) for high-tech products and processes, which provokes a growing release of these metals into the environment and draws attention to biological and ecotoxicological consequences of their increasing concentrations in the environment. However, research has long neglected the environmental behaviour of REY. Coupled with publications including incomplete REY sets or data of questionable analytical quality, many open questions remain.
This dissertation investigates samples from the biosphere and from the hydrosphere to shed light on the REY transfer at their interface. Duckweeds, widely occurring small water plants, and Norwegian fjord waters together with Baltic Sea outflow samples were chosen as main study objects from the biosphere and the hydrosphere, respectively.
The findings of the biosphere-focused part improve the characterisation of the duckweed reference material BCR-670 (Lemna minor) and highlight the necessity of comparable sample processing for validation of data quality.
All naturally grown duckweeds investigated are REY quasi-hyperaccumulators and share similarly shaped, mildly fractionated shale-normalised REY patterns without positive anthropogenic Gd anomalies, regardless of whether they grew in waters with or without anomalous Gd enrichment.
The hydrosphere-focused part presents the first evidence for constant anthropogenic Gd input into the Baltic Sea outflow. The data combined with literature data further suggest that this signal is transported to southern Norway. In future, it may reach fjord waters further north along the Norwegian coast.
Overall, this dissertation provides important new information about the fate of geogenic and anthropogenic REY at the hydrosphere-biosphere interface and highlights the relevance of basic research as the basis for understanding the complex REY transfer mechanisms across environmental compartments
Digitalization and Lean Management as Tools for Increasing Efficiency in the Transport Industry
No full textThe essay explores the integration of digitalization and Lean management in the transport industry, emphasizing their combined role in enhancing efficiency, flexibility, and sustainability. It outlines the origins of Lean management in the Toyota Production System and its adaptation to logistics and transport through practices such as 5S, Kaizen, and Just-in-Time. The paper highlights successful examples from global companies like DHL, UPS, Delta Airlines, and DB Schenker, demonstrating measurable improvements in productivity and cost reduction.
Digital technologies—including the Internet of Things, Artificial Intelligence, Big Data, and digital twins—strengthen Lean principles by enabling real-time data analysis, automation, and predictive decision-making. The essay also examines Germany’s leadership in transport digitalization and describes practical observations from the Mercedes-Benz plant in Bremen.
Finally, it discusses challenges such as cybersecurity, integration complexity, and ethical concerns, concluding that the synergy between Lean and digitalization forms the foundation for the future of transport—making it smarter, greener, and more resilient in the global economy
The human cytomegalovirus (HCMV) glycoprotein US6 inhibits the cytosolic DNA-induced type I interferon response
No full textThe innate immune response is the first line of defense against viral infection. Host pattern recognition receptors (PRRs) detect pathogen-associated molecular patterns (PAMPs) and trigger several signaling pathways following viral infections. One such pathway, the cGAS-STING pathway, detects cytosolic DNA to induce the production and secretion of type I interferons.
The cGAS-STING pathway is activated by the presence of cytosolic DNA. cGAS (cGMP-AMP synthase), a cytosolic DNA detector, binds double-stranded DNA, dimerizes, and catalyzes the production of the second messenger cyclic GMP-AMP (cGAMP) from GTP and ATP. STING, an ER adaptor protein, binds to cGAMP and becomes activated through dimerization, which results in its trafficking from the ER to the Golgi. At the Golgi, STING recruits the kinase TBK1 and the transcription factor IRF3. TBK1 phosphorylates STING, itself, and IRF3. Phosphorylation activates IRF3, causing its translocation to the nucleus, where it induces the production of type I interferons.
To counteract the host immune response, human cytomegalovirus (HCMV) encodes several immunoevasin proteins. HCMV glycoproteins such as US6 inhibit antigen presentation by blocking peptide transport via the transporter associated with antigen processing (TAP). The region spanning amino acids 89–108 of US6 was identified as responsible for TAP inhibition.
Our studies have identified a novel interaction and function of US6. For the first time, we show that US6 interacts with the host p24 proteins TMED2 and TMED10. US6 inhibits the cytosolic DNA-triggered cGAS-STING pathway and reduces the production of IFNβ1. We have discovered a correlation between the binding of US6 to TMED2 and TMED10 and its ability to inhibit the production of type I interferons. Using sequential mutants, we show that US6 possesses two distinct and separable regions responsible for its functions. Microscopy reveals that US6 delays STING trafficking from the ER to the Golgi
Sentiment Analysis of Tesla Tweets: Leveraging XGBoost for Social Media Insights
No full textThis study conducts an extensive sentiment analysis of 7,357 English Tesla-related tweets using an XGBoost classifier, addressing the critical need to understand public perception of innovative companies in the electric vehicle (EV) sector (Jain et al., 2019). The methodology involves advanced preprocessing with tweet-preprocessor and NLTK, feature engineering using TF-IDF (2,000 features) and weighted VADER sentiment scores, and model optimization via GridSearchCV with SMOTE balancing (Chawla et al., 2002). The model achieved an accuracy of 71.67% and a macro F1-score of 67.73% ± 5.97%, with a sentiment distribution of 37.31% negative, 30.58% neutral, and 32.11% positive. Theoretical assumptions explore the impact of social media on EV sentiment (Thelwall et al., 2010), while results and discussions highlight model performance and Tesla-specific insights (Chen & Guestrin, 2016). The study concludes with implications for EV marketing and future research directions in NLP